Evidence map›Paper›PMID 32579213›Full record

ArticleBioinformatics (Oxford, England)2020

Detecting and correcting misclassified sequences in the large-scale public databases.

Hamid Bagheri, Andrew J Severin, Hridesh Rajan

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

23 citing papers in PubMed.

  1. Article
  2. Genomic insights intoMicrobiology spectrum · 2026
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  3. Review
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  9. Direct On-Chip Diagnostics ofBiomolecules · 2024
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hamid BagheriDepartment of Computer Science, Ames, IA 50011, USA.
Andrew J SeverinGenome Informatics Facility, Iowa State University, Ames, IA 50011, USA.
Hridesh RajanDepartment of Computer Science, Ames, IA 50011, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationAs the cost of sequencing decreases, the amount of data being deposited into public repositories is increasing rapidly. Public databases rely on the user to provide metadata for each submission that is prone to user error. Unfortunately, most public databases, such as non-redundant (NR), rely on user input and do not have methods for identifying errors in the provided metadata, leading to the potential for error propagation. Previous research on a small subset of the NR database analyzed misclassification based on sequence similarity. To the best of our knowledge, the amount of misclassification in the entire database has not been quantified. We propose a heuristic method to detect potentially misclassified taxonomic assignments in the NR database. We applied a curation technique and quality control to find the most probable taxonomic assignment. Our method incorporates provenance and frequency of each annotation from manually and computationally created databases and clustering information at 95% similarity.

resultsWe found more than two million potentially taxonomically misclassified proteins in the NR database. Using simulated data, we show a high precision of 97% and a recall of 87% for detecting taxonomically misclassified proteins. The proposed approach and findings could also be applied to other databases. AVAILABILITY AND IMPLEMENTATION: Source code, dataset, documentation, Jupyter notebooks and Docker container are available at https://github.com/boalang/nr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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MetadataSoftwareDatabases, Factual

Identifiers

PMID32579213
PMCPMC7821992

What OpenQuestion holds

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.